





'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
_a   :					Optional[int]           =       logging.getLogger(__name__)





def   _lowerCAmelCase		(		lowercase      )   ->   Union[str, Any]:
			__lowerCAmelCase   		=							git.Repo(search_parent_directories=lowerCAmelCase__      )
			__lowerCAmelCase   		=							{
			    """repo_id""": str(lowerCAmelCase__      ),
			    """repo_sha""": str(repo.head.object.hexsha      ),
			    """repo_branch""": str(repo.active_branch      ),
			}

			with open(os.path.join(lowerCAmelCase__   ,      """git_log.json"""      )   ,      """w"""      ) as f:
						json.dump(lowerCAmelCase__   ,      lowerCAmelCase__   ,      indent=4      )





def   _lowerCAmelCase		(		lowercase      )   ->   Dict:


			if params.n_gpu <= 0:
						__lowerCAmelCase   		=							0
						__lowerCAmelCase   		=							-1
						__lowerCAmelCase   		=							True
						__lowerCAmelCase   		=							False
						return

			assert torch.cuda.is_available()

			logger.info("""Initializing GPUs"""      )
			if params.n_gpu > 1:
						assert params.local_rank != -1

						__lowerCAmelCase   		=							int(os.environ["""WORLD_SIZE"""]      )
						__lowerCAmelCase   		=							int(os.environ["""N_GPU_NODE"""]      )
						__lowerCAmelCase   		=							int(os.environ["""RANK"""]      )

						# number of nodes / node ID
						__lowerCAmelCase   		=							params.world_size // params.n_gpu_per_node
						__lowerCAmelCase   		=							params.global_rank // params.n_gpu_per_node
						__lowerCAmelCase   		=							True

						assert params.n_nodes == int(os.environ["""N_NODES"""]      )
						assert params.node_id == int(os.environ["""NODE_RANK"""]      )

			# local job (single GPU)
			else:
						assert params.local_rank == -1

						__lowerCAmelCase   		=							1
						__lowerCAmelCase   		=							0
						__lowerCAmelCase   		=							0
						__lowerCAmelCase   		=							0
						__lowerCAmelCase   		=							1
						__lowerCAmelCase   		=							1
						__lowerCAmelCase   		=							False

			# sanity checks
			assert params.n_nodes >= 1
			assert 0 <= params.node_id < params.n_nodes
			assert 0 <= params.local_rank <= params.global_rank < params.world_size
			assert params.world_size == params.n_nodes * params.n_gpu_per_node

			# define whether this is the master process / if we are in multi-node distributed mode
			__lowerCAmelCase   		=							params.node_id == 0 and params.local_rank == 0
			__lowerCAmelCase   		=							params.n_nodes > 1

			# summary
			__lowerCAmelCase   		=							f'--- Global rank: {params.global_rank} - '
			logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes      )
			logger.info(PREFIX + """Node ID        : %i""" % params.node_id      )
			logger.info(PREFIX + """Local rank     : %i""" % params.local_rank      )
			logger.info(PREFIX + """World size     : %i""" % params.world_size      )
			logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node      )
			logger.info(PREFIX + """Master         : %s""" % str(params.is_master      )      )
			logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node      )      )
			logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu      )      )
			logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()      )

			# set GPU device
			torch.cuda.set_device(params.local_rank      )

			# initialize multi-GPU
			if params.multi_gpu:
						logger.info("""Initializing PyTorch distributed"""      )
						torch.distributed.init_process_group(
						    init_method="""env://"""   ,      backend="""nccl"""   ,      )







def   _lowerCAmelCase		(		lowercase      )   ->   Optional[int]:
			np.random.seed(args.seed      )
			torch.manual_seed(args.seed      )
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed      )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  List[Any]			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''timm_backbone'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_,   )       ->   Tuple:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     backbone
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		Optional[Any]							      =     features_only
						UpperCAmelCase_:		Any							      =     use_pretrained_backbone
						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     out_indices if out_indices is not None else (-1,)






